This course covers a range of introductory statistical topics and uses SAS software to carry out analysis. Emphasis will be placed on the interpretation of the results. It covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).
Ready-to-use procedures handle a wide range of statistical techniques including simple descriptive statistics, data visualization, analysis of variance, regression, categorical data analysis, multivariate analysis, cluster analysis, and non parametric analysis are part of this program

One attempt at taking the SAS Enterprise Miner Certification Exam is included with this training.

Learn how to

generate descriptive statistics and explore data with graphs

perform analysis of variance

perform linear regression and assess the assumptions

use diagnostic statistics to identify potential outliers in multiple regression

use chi-square statistics to detect associations among categorical variables

fit a multiple logistic regression model

define a SAS Enterprise Miner project and explore data graphically

modify data for better analysis results

build and understand predictive models such as decision trees and regression models

compare and explain complex models

generate and use score code

apply association and sequence discovery to transaction data

use other modeling tools such as rule induction, gradient boosting, and support vector machines.

Who should attend

Statisticians and business analysts who want to use a point-and-click interface to SAS; as well as data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner

Formats available

Duration

Before attending this course, you should have knowledge in statistics covering p-values, hypothesis testing, analysis of variance, and regression. In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling.